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  <section id="serving-a-torch-tensorrt-model-with-triton">
<span id="serving-torch-tensorrt-with-triton"></span><h1>Serving a Torch-TensorRT model with Triton<a class="headerlink" href="#serving-a-torch-tensorrt-model-with-triton" title="Permalink to this headline">¶</a></h1>
<p>Optimization and deployment go hand in hand in a discussion about Machine
Learning infrastructure. Once network level optimzation are done
to get the maximum performance, the next step would be to deploy it.</p>
<p>However, serving this optimized model comes with it’s own set of considerations
and challenges like: building an infrastructure to support concorrent model
executions, supporting clients over HTTP or gRPC and more.</p>
<p>The <a class="reference external" href="https://github.com/triton-inference-server/server">Triton Inference Server</a>
solves the aforementioned and more. Let’s discuss step-by-step, the process of
optimizing a model with Torch-TensorRT, deploying it on Triton Inference
Server, and building a client to query the model.</p>
<section id="step-1-optimize-your-model-with-torch-tensorrt">
<h2>Step 1: Optimize your model with Torch-TensorRT<a class="headerlink" href="#step-1-optimize-your-model-with-torch-tensorrt" title="Permalink to this headline">¶</a></h2>
<p>Most Torch-TensorRT users will be familiar with this step. For the purpose of
this demonstration, we will be using a ResNet50 model from Torchhub.</p>
<p>Let’s first pull the <a class="reference external" href="https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch">NGC PyTorch Docker container</a>. You may need to create
an account and get the API key from <a class="reference external" href="https://ngc.nvidia.com/setup/">here</a>.
Sign up and login with your key (follow the instructions
<a class="reference external" href="https://ngc.nvidia.com/setup/api-key">here</a> after signing up).</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="cp"># &lt;xx.xx&gt; is the yy:mm for the publishing tag for NVIDIA&#39;s Pytorch</span>
<span class="cp"># container; eg. 22.04</span>

<span class="n">docker</span><span class="w"> </span><span class="n">run</span><span class="w"> </span><span class="o">-</span><span class="n">it</span><span class="w"> </span><span class="o">--</span><span class="n">gpus</span><span class="w"> </span><span class="n">all</span><span class="w"> </span><span class="o">-</span><span class="n">v</span><span class="w"> </span><span class="n">$</span><span class="p">{</span><span class="n">PWD</span><span class="p">}</span><span class="o">:/</span><span class="n">scratch_space</span><span class="w"> </span><span class="n">nvcr</span><span class="p">.</span><span class="n">io</span><span class="o">/</span><span class="n">nvidia</span><span class="o">/</span><span class="n">pytorch</span><span class="o">:&lt;</span><span class="n">xx</span><span class="p">.</span><span class="n">xx</span><span class="o">&gt;-</span><span class="n">py3</span><span class="w"></span>
<span class="n">cd</span><span class="w"> </span><span class="o">/</span><span class="n">scratch_space</span><span class="w"></span>
</pre></div>
</div>
<p>Once inside the container, we can proceed to download a ResNet model from
Torchhub and optimize it with Torch-TensorRT.</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span>import torch
import torch_tensorrt
torch.hub._validate_not_a_forked_repo=lambda a,b,c: True

# load model
model = torch.hub.load(&#39;pytorch/vision:v0.10.0&#39;, &#39;resnet50&#39;, pretrained=True).eval().to(&quot;cuda&quot;)

# Compile with Torch TensorRT;
trt_model = torch_tensorrt.compile(model,
    inputs= [torch_tensorrt.Input((1, 3, 224, 224))],
    enabled_precisions= { torch.half} # Run with FP32
)

# Save the model
torch.jit.save(trt_model, &quot;model.pt&quot;)
</pre></div>
</div>
<p>After copying the model, exit the container. The next step in the process
is to set up a Triton Inference Server.</p>
</section>
<section id="step-2-set-up-triton-inference-server">
<h2>Step 2: Set Up Triton Inference Server<a class="headerlink" href="#step-2-set-up-triton-inference-server" title="Permalink to this headline">¶</a></h2>
<p>If you are new to the Triton Inference Server and want to learn more, we
highly recommend to checking our <a class="reference external" href="https://github.com/triton-inference-server">Github
Repository</a>.</p>
<p>To use Triton, we need to make a model repository. A model repository, as the
name suggested, is a repository of the models the Inference server hosts. While
Triton can serve models from multiple repositories, in this example, we will
discuss the simplest possible form of the model repository.</p>
<p>The structure of this repository should look something like this:</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">model_repository</span><span class="w"></span>
<span class="o">|</span><span class="w"></span>
<span class="o">+--</span><span class="w"> </span><span class="n">resnet50</span><span class="w"></span>
<span class="w">    </span><span class="o">|</span><span class="w"></span>
<span class="w">    </span><span class="o">+--</span><span class="w"> </span><span class="n">config</span><span class="p">.</span><span class="n">pbtxt</span><span class="w"></span>
<span class="w">    </span><span class="o">+--</span><span class="w"> </span><span class="mi">1</span><span class="w"></span>
<span class="w">        </span><span class="o">|</span><span class="w"></span>
<span class="w">        </span><span class="o">+--</span><span class="w"> </span><span class="n">model</span><span class="p">.</span><span class="n">pt</span><span class="w"></span>
</pre></div>
</div>
<p>There are two files that Triton requires to serve the model: the model itself
and a model configuration file which is typically provided in <code class="docutils literal notranslate"><span class="pre">config.pbtxt</span></code>.
For the model we prepared in step 1, the following configuration can be used:</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="nl">name</span><span class="p">:</span><span class="w"> </span><span class="s">&quot;resnet50&quot;</span><span class="w"></span>
<span class="nl">platform</span><span class="p">:</span><span class="w"> </span><span class="s">&quot;pytorch_libtorch&quot;</span><span class="w"></span>
<span class="nl">max_batch_size</span><span class="w"> </span><span class="p">:</span><span class="w"> </span><span class="mi">0</span><span class="w"></span>
<span class="n">input</span><span class="w"> </span><span class="p">[</span><span class="w"></span>
<span class="w">  </span><span class="p">{</span><span class="w"></span>
<span class="w">    </span><span class="nl">name</span><span class="p">:</span><span class="w"> </span><span class="s">&quot;input__0&quot;</span><span class="w"></span>
<span class="w">    </span><span class="nl">data_type</span><span class="p">:</span><span class="w"> </span><span class="n">TYPE_FP32</span><span class="w"></span>
<span class="w">    </span><span class="nl">dims</span><span class="p">:</span><span class="w"> </span><span class="p">[</span><span class="w"> </span><span class="mi">3</span><span class="p">,</span><span class="w"> </span><span class="mi">224</span><span class="p">,</span><span class="w"> </span><span class="mi">224</span><span class="w"> </span><span class="p">]</span><span class="w"></span>
<span class="w">    </span><span class="n">reshape</span><span class="w"> </span><span class="p">{</span><span class="w"> </span><span class="n">shape</span><span class="o">:</span><span class="w"> </span><span class="p">[</span><span class="w"> </span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">3</span><span class="p">,</span><span class="w"> </span><span class="mi">224</span><span class="p">,</span><span class="w"> </span><span class="mi">224</span><span class="w"> </span><span class="p">]</span><span class="w"> </span><span class="p">}</span><span class="w"></span>
<span class="w">  </span><span class="p">}</span><span class="w"></span>
<span class="p">]</span><span class="w"></span>
<span class="n">output</span><span class="w"> </span><span class="p">[</span><span class="w"></span>
<span class="w">  </span><span class="p">{</span><span class="w"></span>
<span class="w">    </span><span class="nl">name</span><span class="p">:</span><span class="w"> </span><span class="s">&quot;output__0&quot;</span><span class="w"></span>
<span class="w">    </span><span class="nl">data_type</span><span class="p">:</span><span class="w"> </span><span class="n">TYPE_FP32</span><span class="w"></span>
<span class="w">    </span><span class="nl">dims</span><span class="p">:</span><span class="w"> </span><span class="p">[</span><span class="w"> </span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">1000</span><span class="w"> </span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">1</span><span class="p">]</span><span class="w"></span>
<span class="w">    </span><span class="n">reshape</span><span class="w"> </span><span class="p">{</span><span class="w"> </span><span class="n">shape</span><span class="o">:</span><span class="w"> </span><span class="p">[</span><span class="w"> </span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">1000</span><span class="w"> </span><span class="p">]</span><span class="w"> </span><span class="p">}</span><span class="w"></span>
<span class="w">  </span><span class="p">}</span><span class="w"></span>
<span class="p">]</span><span class="w"></span>
</pre></div>
</div>
<p>The <code class="docutils literal notranslate"><span class="pre">config.pbtxt</span></code> file is used to describe the exact model configuration
with details like the names and shapes of the input and output layer(s),
datatypes, scheduling and batching details and more. If you are new to Triton,
we highly encourage you to check out this <a class="reference external" href="https://github.com/triton-inference-server/server/blob/main/docs/model_configuration.md">section of our
documentation</a>
for more details.</p>
<p>With the model repository setup, we can proceed to launch the Triton server
with the docker command below. Refer <a class="reference external" href="https://catalog.ngc.nvidia.com/orgs/nvidia/containers/tritonserver">this page</a> for the pull tag for the container.</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="cp"># Make sure that the TensorRT version in the Triton container</span>
<span class="cp"># and TensorRT version in the environment used to optimize the model</span>
<span class="cp"># are the same.</span>

<span class="n">docker</span><span class="w"> </span><span class="n">run</span><span class="w"> </span><span class="o">--</span><span class="n">gpus</span><span class="w"> </span><span class="n">all</span><span class="w"> </span><span class="o">--</span><span class="n">rm</span><span class="w"> </span><span class="o">-</span><span class="n">p</span><span class="w"> </span><span class="mi">8000</span><span class="o">:</span><span class="mi">8000</span><span class="w"> </span><span class="o">-</span><span class="n">p</span><span class="w"> </span><span class="mi">8001</span><span class="o">:</span><span class="mi">8001</span><span class="w"> </span><span class="o">-</span><span class="n">p</span><span class="w"> </span><span class="mi">8002</span><span class="o">:</span><span class="mi">8002</span><span class="w"> </span><span class="o">-</span><span class="n">v</span><span class="w"> </span><span class="o">/</span><span class="n">full</span><span class="o">/</span><span class="n">path</span><span class="o">/</span><span class="n">to</span><span class="o">/</span><span class="n">the_model_repository</span><span class="o">/</span><span class="n">model_repository</span><span class="o">:/</span><span class="n">models</span><span class="w"> </span><span class="n">nvcr</span><span class="p">.</span><span class="n">io</span><span class="o">/</span><span class="n">nvidia</span><span class="o">/</span><span class="n">tritonserver</span><span class="o">:&lt;</span><span class="n">xx</span><span class="p">.</span><span class="n">yy</span><span class="o">&gt;-</span><span class="n">py3</span><span class="w"> </span><span class="n">tritonserver</span><span class="w"> </span><span class="o">--</span><span class="n">model</span><span class="o">-</span><span class="n">repository</span><span class="o">=/</span><span class="n">models</span><span class="w"></span>
</pre></div>
</div>
<p>This should spin up a Triton Inference server. Next step, building a simple
http client to query the server.</p>
</section>
<section id="step-3-building-a-triton-client-to-query-the-server">
<h2>Step 3: Building a Triton Client to Query the Server<a class="headerlink" href="#step-3-building-a-triton-client-to-query-the-server" title="Permalink to this headline">¶</a></h2>
<p>Before proceeding, make sure to have a sample image on hand. If you don’t
have one, download an example image to test inference. In this section, we
will be going over a very basic client. For a variety of more fleshed out
examples, refer to the <a class="reference external" href="https://github.com/triton-inference-server/client/tree/main/src/python/examples">Triton Client Repository</a></p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">wget</span><span class="w">  </span><span class="o">-</span><span class="n">O</span><span class="w"> </span><span class="n">img1</span><span class="p">.</span><span class="n">jpg</span><span class="w"> </span><span class="s">&quot;https://www.hakaimagazine.com/wp-content/uploads/header-gulf-birds.jpg&quot;</span><span class="w"></span>
</pre></div>
</div>
<p>We then need to install dependencies for building a python client. These will
change from client to client. For a full list of all languages supported by Triton,
please refer to <a class="reference external" href="https://github.com/triton-inference-server/client">Triton’s client repository</a>.</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span><span class="w"> </span><span class="n">install</span><span class="w"> </span><span class="n">torchvision</span><span class="w"></span>
<span class="n">pip</span><span class="w"> </span><span class="n">install</span><span class="w"> </span><span class="n">attrdict</span><span class="w"></span>
<span class="n">pip</span><span class="w"> </span><span class="n">install</span><span class="w"> </span><span class="n">nvidia</span><span class="o">-</span><span class="n">pyindex</span><span class="w"></span>
<span class="n">pip</span><span class="w"> </span><span class="n">install</span><span class="w"> </span><span class="n">tritonclient</span><span class="p">[</span><span class="n">all</span><span class="p">]</span><span class="w"></span>
</pre></div>
</div>
<p>Let’s jump into the client. Firstly, we write a small preprocessing function to
resize and normalize the query image.</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">import</span><span class="w"> </span><span class="n">numpy</span><span class="w"> </span><span class="n">as</span><span class="w"> </span><span class="n">np</span><span class="w"></span>
<span class="n">from</span><span class="w"> </span><span class="n">torchvision</span><span class="w"> </span><span class="k">import</span><span class="w"> </span><span class="n">transforms</span><span class="w"></span>
<span class="n">from</span><span class="w"> </span><span class="n">PIL</span><span class="w"> </span><span class="k">import</span><span class="w"> </span><span class="n">Image</span><span class="w"></span>
<span class="k">import</span><span class="w"> </span><span class="n">tritonclient</span><span class="p">.</span><span class="n">http</span><span class="w"> </span><span class="n">as</span><span class="w"> </span><span class="n">httpclient</span><span class="w"></span>
<span class="n">from</span><span class="w"> </span><span class="n">tritonclient</span><span class="p">.</span><span class="n">utils</span><span class="w"> </span><span class="k">import</span><span class="w"> </span><span class="n">triton_to_np_dtype</span><span class="w"></span>

<span class="cp"># preprocessing function</span>
<span class="n">def</span><span class="w"> </span><span class="n">rn50_preprocess</span><span class="p">(</span><span class="n">img_path</span><span class="o">=</span><span class="s">&quot;img1.jpg&quot;</span><span class="p">)</span><span class="o">:</span><span class="w"></span>
<span class="w">    </span><span class="n">img</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">Image</span><span class="p">.</span><span class="n">open</span><span class="p">(</span><span class="n">img_path</span><span class="p">)</span><span class="w"></span>
<span class="w">    </span><span class="n">preprocess</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">transforms</span><span class="p">.</span><span class="n">Compose</span><span class="p">([</span><span class="w"></span>
<span class="w">        </span><span class="n">transforms</span><span class="p">.</span><span class="n">Resize</span><span class="p">(</span><span class="mi">256</span><span class="p">),</span><span class="w"></span>
<span class="w">        </span><span class="n">transforms</span><span class="p">.</span><span class="n">CenterCrop</span><span class="p">(</span><span class="mi">224</span><span class="p">),</span><span class="w"></span>
<span class="w">        </span><span class="n">transforms</span><span class="p">.</span><span class="n">ToTensor</span><span class="p">(),</span><span class="w"></span>
<span class="w">        </span><span class="n">transforms</span><span class="p">.</span><span class="n">Normalize</span><span class="p">(</span><span class="n">mean</span><span class="o">=</span><span class="p">[</span><span class="mf">0.485</span><span class="p">,</span><span class="w"> </span><span class="mf">0.456</span><span class="p">,</span><span class="w"> </span><span class="mf">0.406</span><span class="p">],</span><span class="w"> </span><span class="n">std</span><span class="o">=</span><span class="p">[</span><span class="mf">0.229</span><span class="p">,</span><span class="w"> </span><span class="mf">0.224</span><span class="p">,</span><span class="w"> </span><span class="mf">0.225</span><span class="p">]),</span><span class="w"></span>
<span class="w">    </span><span class="p">])</span><span class="w"></span>
<span class="w">    </span><span class="k">return</span><span class="w"> </span><span class="n">preprocess</span><span class="p">(</span><span class="n">img</span><span class="p">).</span><span class="n">numpy</span><span class="p">()</span><span class="w"></span>

<span class="n">transformed_img</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">rn50_preprocess</span><span class="p">()</span><span class="w"></span>
</pre></div>
</div>
<p>Building a client requires three basic points. Firstly, we setup a connection
with the Triton Inference Server.</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="cp"># Setting up client</span>
<span class="n">client</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">httpclient</span><span class="p">.</span><span class="n">InferenceServerClient</span><span class="p">(</span><span class="n">url</span><span class="o">=</span><span class="s">&quot;localhost:8000&quot;</span><span class="p">)</span><span class="w"></span>
</pre></div>
</div>
<p>Secondly, we specify the names of the input and output layer(s) of our model.</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">inputs</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">httpclient</span><span class="p">.</span><span class="n">InferInput</span><span class="p">(</span><span class="s">&quot;input__0&quot;</span><span class="p">,</span><span class="w"> </span><span class="n">transformed_img</span><span class="p">.</span><span class="n">shape</span><span class="p">,</span><span class="w"> </span><span class="n">datatype</span><span class="o">=</span><span class="s">&quot;FP32&quot;</span><span class="p">)</span><span class="w"></span>
<span class="n">inputs</span><span class="p">.</span><span class="n">set_data_from_numpy</span><span class="p">(</span><span class="n">transformed_img</span><span class="p">,</span><span class="w"> </span><span class="n">binary_data</span><span class="o">=</span><span class="n">True</span><span class="p">)</span><span class="w"></span>

<span class="n">outputs</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">httpclient</span><span class="p">.</span><span class="n">InferRequestedOutput</span><span class="p">(</span><span class="s">&quot;output__0&quot;</span><span class="p">,</span><span class="w"> </span><span class="n">binary_data</span><span class="o">=</span><span class="n">True</span><span class="p">,</span><span class="w"> </span><span class="n">class_count</span><span class="o">=</span><span class="mi">1000</span><span class="p">)</span><span class="w"></span>
</pre></div>
</div>
<p>Lastly, we send an inference request to the Triton Inference Server.</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span># Querying the server
results = client.infer(model_name=&quot;resnet50&quot;, inputs=[inputs], outputs=[outputs])
inference_output = results.as_numpy(&#39;output__0&#39;)
print(inference_output[:5])
</pre></div>
</div>
<p>The output of the same should look like below:</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span>[b&#39;12.468750:90&#39; b&#39;11.523438:92&#39; b&#39;9.664062:14&#39; b&#39;8.429688:136&#39;
 b&#39;8.234375:11&#39;]
</pre></div>
</div>
<p>The output format here is <code class="docutils literal notranslate"><span class="pre">&lt;confidence_score&gt;:&lt;classification_index&gt;</span></code>.
To learn how to map these to the label names and more, refer to Triton Inference Server’s
<a class="reference external" href="https://github.com/triton-inference-server/server/blob/main/docs/protocol/extension_classification.md">documentation</a>.</p>
</section>
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